Field
The present invention relates to a system and a method for an autonomous vehicle. Such an autonomous vehicle may in particular be a self-propelled lawn mower or service robot such as a vacuum cleaner. Particularly, the control system and corresponding method and vehicle relate to the generation of control signals for finding a travel path where recognizing obstacles in the travel path is difficult due to difficult light conditions.
Description of the Related Art
The use of autonomous vehicles that assist people in performing work such as cleaning buildings or mowing a lawn is already known in the art. Initially, the autonomous vehicles have been guided by using for example a wiring that indicates the edges of an area where the autonomous vehicle can travel on its own. This is a great increase in comfort for a user, because he does not need to supervise the autonomous vehicle at any time of operation. But of course, the edges of an area to be mowed or where vacuum cleaning shall be performed are not the only boundaries. In particular, lawn mowers need to react on obstacles that are on a lawn such as toys that children playing on the lawn have left there. Early attempts used bump sensors to detect such obstacles. In case that a collision is recognized between the autonomous vehicle and the obstacles the direction of travel will be changed and thus, it is avoided that the autonomous vehicle is manoeuvred to a dead end. But of course there may be obstacles that cannot be sensed by such bump sensors, because there are flat obstacles lying in the grass, or on the ground in case of a vacuum cleaner. An example may be a mobile telephone the height of which is not sufficient in order to cause a collision and thus, the bump sensor to recognize the obstacle.
Thus, image processing systems were developed that use a camera for capturing images of the environment, in particular in the direction of travel of the autonomous vehicle. Obstacle detection algorithms have been developed which are capable of determining an object that lies in the travel path of the autonomous vehicle on the basis of the captured images. The vehicle is equipped with an image capture means that captures a sequence of images and provides then to a processing means which then detects an obstacle by distinguishing areas that are not recognized as grass or ground.
The problem of such obstacle detection systems is, however, that the light conditions under which the images are captured may be difficult in particular if the autonomous vehicle is intended for outdoor use. For example, during day time there may be sufficient light in order to recognize obstacles based on the mentioned detection algorithms, but in the evening when it gets darker, the obstacles may not be recognized reliably. Image capturing means such as video cameras may then be adapted to increase an exposure time in order to achieve an image with a sufficient intensity over all the pixels of the image. But still there are some problems to detect objects with a sufficient reliability in case that the light conditions are not similar in the entire area of the environment which is captured by the image capturing means. For example, in bright sunlight there is a big difference between areas where a shadow is and an area which lies in bright sunlight.
Being aware of such problematic light conditions, new systems have been developed as for example described in EP 2 620 050 A1. Here it is suggested to adapt the setting of the camera. In addition, an additional light source may be used which is switched on, if the environment becomes too dark and thus, the autonomous vehicle travels along a path where the light conditions change sufficient illumination of the area can be achieved. The adaptation of the camera setting with respect to exposure time and gain can be performed in a time series manner. But still images that at the same time shall provide information about dark areas and bright areas are problematic. One attempt to solve this problem is using a so-called bracketing technique in which two images are taken with different camera settings, one optimized for the dark area and one for the bright area. Based on these two images that are taken with different camera settings, a so-called HDR-image is generated. But known HDR-techniques combine the two images that are taken at different camera settings and generate one single HDR image which is then used for further analysis. Doing so causes artifacts in the combined HDR-image and therefore, detection of obstacles that for example lie at the boundary between the dark area and the bright area may still be difficult, because artifacts in particular occur at these boundaries. Although there are known a plurality of different approaches for generating such an HDR-image, they all suffer from the same problem that a single image is provided which is a combination of areas that have been illuminated differently.
This is in particular obvious when the camera that takes the different pictures is moving and thus there is time difference between the two images that are to be combined. Here, it becomes perfectly clear that the combined image will include artifacts which then of course cause problems during the further processing of the image.